Systems and methods for estimating irradiation dose for patient application based on a radiation field model and a patient anatomy model

ABSTRACT

Systems and methods for estimating irradiation dose for patient application based on a radiation field model and a patient anatomy model are disclosed. According to an aspect, a method includes providing a database of patient anatomy models. The method also includes providing a radiation field model of an X-ray system. Further, the method includes receiving a measure of an anatomy of a patient. The method also includes determining a patient anatomy model among the patient anatomy models that matches or is similar to the anatomy of the patient based on the measure of the patient and a corresponding measure of each of the patient anatomy models. The method also includes estimating an irradiation dose for application to the patient by the X-ray system based on the radiation field model and the determined patient anatomy model.

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Patent Application62/235,659, filed Oct. 1, 2015, and titled SYSTEMS AND METHODS FORESTIMATING IRRADIATION DOSE FOR PATIENT APPLICATION BASED ON A RADIATIONFIELD MODEL AND A PATIENT ANATOMY MODEL, the entire content of which isincorporated herein by reference in its entirety.

FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under grant R01 EB001838awarded by National Institutes of Health (NIH). The government hascertain rights to the invention.

TECHNICAL FIELD

The present disclosure relates to imaging. More particularly, thepresent disclosure relates to estimation of irradiation dose for patientapplication based on a radiation field model and a patient anatomymodel.

BACKGROUND

A computed tomography (CT) scan makes use of computer-processedcombinations of X-ray images captured from different angles to producecross-sectional (tomographic) images of specific areas of a scannedobject. Medical imaging is a common application of X-ray CT. Itscross-sectional images are used for diagnostic and therapeutic purposesin various medical disciplines for patient care.

Reduction of radiation dose during CT examinations without compromisingimage quality is an important issue. Generally, higher radiation dosesresult in higher-resolution images, while lower doses lead to increasedimage noise and artifactual images. However, increased dosage canincrease the risk of adverse side effects, including the risk ofradiation induced cancer. Several approaches have been used to reduceradiation exposure during CT examinations. However, there is acontinuing need to provide improved systems and techniques foraccurately determining and subsequently optimizing (i.e., reducing)patient radiation dose during CT examinations while also obtaininghigh-quality CT images.

SUMMARY

Disclosed herein are systems and methods for estimating irradiation dosefor patient application based on a radiation field model and a patientanatomy model. According to an aspect, a method includes providing adatabase of patient anatomy models. The method also includes providing aradiation field model of an X-ray system. Further, the method includesreceiving a measure of an anatomy of a patient. The method also includesdetermining a patient anatomy model among the patient anatomy modelsthat matches or is similar to the anatomy of the patient based on themeasure of the patient and a corresponding measure of each of thepatient anatomy models. The method also includes estimating anirradiation dose for application to the patient by the X-ray systembased on the radiation field model and the determined patient anatomymodel.

BRIEF DESCRIPTION OF THE DRAWINGS

The summary above, as well as the following detailed description ofillustrative embodiments, is better understood when read in conjunctionwith the appended drawings. For the purpose of illustrating the presentdisclosure, example constructions of the disclosure are shown in thedrawings. However, the present disclosure is not limited to specificmethods and instrumentalities disclosed herein. Moreover, those in theart will understand that the drawings are not to scale or reflect exactmathematical dependencies. Wherever possible, like elements have beenindicated by identical numbers.

Embodiments of the present disclosure will now be described, by way ofexample only, with reference to the following diagrams in which:

FIG. 1 is a schematic diagram of a CT imaging system for estimatingirradiation dose for patient application based on a radiation fieldmodel and a patient anatomy model in accordance with embodiments of thepresent disclosure;

FIG. 2 is a flowchart of an example method for estimating irradiationdose for patient application based on a radiation field model and apatient anatomy model in accordance with embodiments of the presentdisclosure;

FIG. 3 are images of three-dimensional (3D) frontal views of examplepatient models used in a study (arms can be raised or lowered forsimulation of body scans);

FIG. 4 depicts an image illustrating two pairs of matched models;

FIG. 5 is a graph showing the dose rate profile over an infinitely longCTDI phantom for a thin beam (25 mm full width at half maximum)generated by Monte Carlo simulation, example tube current profiles ofTCM and constant tube current scans, a Z dimensional accumulated doseprofile for TCM and a constant tube current scan derived by convolvingthe dose rate profile with tube current profiles, and the dose ratioderived by dividing the accumulated dose profile of a TCM scan by aconstant tube current scan;

FIG. 6 are graphs showing a histogram of error in predicting organ dosefor the abdominopelvic scans;

FIG. 7 is a screen display of an example progress report showing awritten record in accordance with embodiments of the present disclosure;and

FIG. 8 illustrates a graph showing an example relationship of theuncertainty of organ dose estimation to an average distance ofmeasurement mismatch between an actual patient and matched models.

DETAILED DESCRIPTION

The following detailed description illustrates embodiments of thepresent disclosure and manners by which they can be implemented.

The example functions and methods disclosed herein may be implemented byany suitable X-ray system, such as a CT imaging system. A CT imagingsystem may use a three-dimensional (3D) rotational X-ray examinationdevice having one or more X-ray tubes and the same number of detectorsplaced on a gantry that is rotated around a patient. Other types of CTimaging systems may use 3D examination devices equipped with astationary detector ring with multiple pixels that is mounted on thestator of the gantry, and only the X-ray tube is rotating with the rotorof the gantry around the patient. The CT scanning may be based on arectilinear propagation and attenuation of X-rays. The CT imaging systemmay thereby acquire a series of X-ray projections from a range of anglesaround the subject. Each projection can represent the value (orcollection of values in a multi-element X-ray detector) of the X-rayattenuation line integral through the object along the line from anX-ray source to an X-ray detector. Imaging an object to be graphicallyreconstructed at equiangular-spaced views over 180° forms a complete setof projection data. Tomographic image reconstruction can create atwo-dimensional (2D) image (or 3D volume) from the measured projectiondata.

As a fundamental step to manage and optimize radiation dose, it isimportant to quantify patient-specific organ dose. Such dose estimatescan provide information useful for the design of individualized CTprotocols, for the assessment and improvement of patient imagingmanagement decisions, and for optimizing CT dose in relationship withimage quality of the study.

For the purposes herein, a patient may be defined as a human being ofany age, gender, body habitus, and/or pregnancy status. For theavoidance of doubt, a patient may be, but is not limited to, a child, anadult, or a pregnant woman.

FIG. 1 illustrates a schematic diagram of a CT imaging system 100 forestimating irradiation dose for patient application based on a radiationfield model and a patient anatomy model in accordance with embodimentsof the present disclosure. Referring to FIG. 1, the system 100 includesa rotational gantry 101 that is rotatable about a longitudinal axis 108of a patient's body 107 or any other object to be examined. The gantry101 may include one or more X-ray sources or tubes 102 that areconfigured to project a beam of X-rays 106 towards an X-ray detectorarray 103 placed at the opposite side of the gantry 101. The X-raydetector array 103 can be equipped with multiple detector elements 103 awhich can together sense the projected X-rays passing through thepatient's body 107 to be examined between X-ray detector array 103 andX-ray source 102. Each detector element 103 a can generate an electricalsignal that represents the intensity of an impinging X-ray beam and canhence be used to estimate the attenuation of the beam as it passesthrough the object.

In a rotational CT scanner such as depicted in FIG. 1, a 3D volume canbe calculated by reconstructing and stacking individual 2D slices. SomeCT imaging systems can employ 2D detector arrays, allowing theacquisition of truly 3D data sets. In this particular example, only asingle row of detector elements 103 a is shown (i.e., a detector row).However, a multi-slice detector array such as denoted by referencenumber 103 include multiple parallel rows of detector elements 103 asuch that projection data corresponding to multiple quasi-parallel orparallel slices can be acquired simultaneously during a scan. Thedetector elements 103 a may completely encircle the patient 107. Thisfigure shows only a single X-ray source 102, but it should be understoodthat multiple X-ray sources may be positioned around gantry 101.

Operation of X-ray source 102 can be governed by a control mechanism 109of the system 100. Control mechanism 109 can include an X-ray controller110 that provides power and timing signals to one or more X-ray sources102. A data acquisition system (DAX) 111 belonging to the controlmechanism 109 can sample analog data from detector elements 103 a andcan convert the data to digital signals for subsequent processing. Animage reconstructor 112 can receive sampled and digitized X-ray datafrom DAS 111 and can perform high-speed image reconstruction. Thereconstructed image can be applied as an input to a computing device 113(e.g., a desktop or laptop computer), which stores the image in a massstorage device 114. The computing device 113 may include hardware,software, firmware, or combinations thereof for implementing thefunctionality described herein. For example, the computing device 113may include one or more processors 130 and memory 132. The imagereconstructor 112 may be specialized hardware residing in the computingdevice 113 or a software program executed by the computing device 113.

The computing device 113 may receive signals via a user interface orgraphical user interface (GUI). Particularly, the computing device 113may receive commands and scanning parameters from a user interface 115that includes, for example, a keyboard and mouse (not shown). Anassociated display 116 can allow an operator to observe thereconstructed image and other data from the computing device 113. Theoperator-supplied commands and parameters can be used by the computingdevice 113 to provide control signals and information to the X-raycontroller 110, DAX 111, and a table motor controller 117 incommunication with a patient table 104, which controls a motorizedpatient table 104 so as to position patient 107 in gantry 101.Particularly, the patient table 104 can move the patient 107 through agantry opening.

The computing device 113 or another suitable computing device may beconfigured to implement the functionality described herein. Moreparticularly, for example, FIG. 2 illustrates a flowchart of an examplemethod for estimating irradiation dose for patient application based ona radiation field model and a patient anatomy model in accordance withembodiments of the present disclosure. Referring to FIG. 2, the memory132 may include suitable instructions executable by the processor(s) 130for implementing the functionality described herein.

The method of FIG. 2 includes providing 200 a database of patientmodels. Referring to FIG. 1 for example, the computing device 113 mayinclude a database 134 of patient models residing in its memory 132 ormay have access to memory that stores the database 134. The patientanatomy models may include patient anatomy models of differing ages,sizes, genders, and/or other characteristics of an individual. To modelthe patient anatomy, the database 134 may have a library ofcomputational phantoms with representative ages, sizes, genders, and/orother characteristics of an individual. The large number of uniquemodels in the library aims to reflect the anatomical variability acrossa population.

An initial model may be first created by segmenting bones and majororgans within a CT image volume. A 3D surface may subsequently be fit topolygon models using non-uniform rational B-splines (NURBS) modelingsoftware (such as the modeling software available from Rhinoceros,McNeel North America, of Seattle, Wash.). Other organs and structuresmay be defined by morphing structures from existing male or female fullbody adult and pediatric models. Volumes of the morphedorgans/structures may be checked and scaled, if desired or needed, tomatch age-interpolated organ volume and anthropometry data. Thefull-body patient models may include most of the radiosensitive organsas understood by those of skill in the art and can be incorporated intosimulation programs for image quality or dose estimation. FIG. 3illustrates images of 3D frontal views of example patient models used ina study with all arms being raised for simulation of body scans.

The method of FIG. 2 includes providing 202 a radiation field model ofan X-ray system. Continuing the aforementioned example, the system 100may store in its memory 132 or have access to memory that stores aradiation field model of the X-ray source 102. In an example, eachradiation field model may be a computational model that is generatedbased on a clinical CT case.

In an example, a radiation field model can be generated by detailedmodeling of one or more geometries of the X-ray system, X-ray tubemotion of the X-ray system, a characteristic of a bowtie filter of theX-ray system, peak kilovoltage (kVp) of the X-ray system, and a peakmilliampere (mA) of the X-ray system. The model can effectively quantifythe heterogeneous dose field created by the change of tube current.

Organ dose under fixed tube current may be simulated using a validatedMonte Carlo simulation program as the estimation basis. Such organ dosevalues may be normalized by CTDIvol and modeled as a function of patientsize to derive the so-called h_(organ). h_(organ) can be regarded as afactor that relates the organ dose values to patient anatomy under aunified dose field (constant tube current condition). It may be used asthe basis for estimating organ dose under an arbitrary dose fielddepending on the detailed TCM profile.

An organ-specific CTDI_(vol) may be further generated to account for theheterogeneous distribution of the dose field under TCM schemes. The dosefield modeling needs to effectively quantify the heterogeneousdistribution created by dynamic tube current changes.

Initially, the dose rate profile of a thin beam (for example, 25-mmfull-width at half-maximum) may be generated by Monte Carlo simulation,depicting the z-dimensional dose distribution for an infinitely longfixed or variable-sized phantom. Subsequently, the dose rate profile maybe convolved with TCM and constant tube current profiles to generate theaccumulated z-dimensional dose distributions under each condition. Thedifference between the accumulated dose distributions under TCM andconstant tube current conditions may be determined and overlaid with thepatient organ distribution. Based on this information, a regionalCTDI_(vol) value can be calculated for each organ to account for thelocal dose field.

Now returning to FIG. 2, the method includes receiving 204 a measure ofan anatomy of a patient. Continuing the aforementioned example of FIG.1, the computing device 113 can receive a measure of an anatomy of apatient. The computing device 113 may receive, for example, a measure ofa distance between the base of the neck through the bottom of thepelvois (i.e., the normal range of a standard chest, abdomen and pelvisCT). For example, with an atlas of computational phantoms that cover abroad range of human anatomy, a new clinical patient can be matched to acorresponding model that closely resembles the patient in terms of majororgan locations. The patient trunk height, defined as the distancebetween the top of clavicle to the end of the pelvic region, may bemeasured from the topogram image of the patient and matched against XCATphantoms in the library.

The method of FIG. 2 includes determining 206 a patient anatomy modelamong the patient anatomy models that matches or is similar to theanatomy of the patient based on the measure of the patient and acorresponding measure of each of the patient anatomy models. As anexample, a patient anatomy model among the patient anatomy models may bedetermined based on a comparison of a measure of the distance betweenthe top of the clavicle and the end of the pelvic region in the anatomymodels. Particularly, for example, the computing device 113 can receiveor determine a distance between the top of the clavicle and the end ofthe pelvic region of the patient. This distance may be an exact,substantially close, or approximation. In addition, the computing device113 can receive or determine the distance between the top of theclavicle and the end of the pelvic region in each of multiple patientanatomy models. The distance for the patient may be compared to themodels to determine one or more models that closely or exactly match thedistance for the patient. It should be noted that any suitable measureor measures may be utilized for matching a patient to one or moresimilar anatomy models for use in accordance with the presentdisclosure.

FIG. 4 depicts an image illustrating two pairs of matched models (maleand female patients at 50% height and weight). As shown at reference (a)of FIG. 4, an example patient-model matching pair as determined by trunkheight for the 50th percentile male is depicted. As shown at reference(b) of FIG. 4, an example patient-model matching pair as determined bytrunk height for the 50^(th) percentile female is depicted.

As shown in FIG. 5, reference character (a) of FIG. 5 illustrates agraph showing the dose rate profile over an infinitely long CTDI phantomfor a thin beam (25 mm full width at half maximum) generated by MonteCarlo simulation. Reference character (b) of FIG. 5 illustrates a graphshowing example tube current profiles of TCM and constant tube currentscans. Reference character (c) of FIG. 5 illustrates a graph showing a Zdimensional accumulated dose profile for TCM and a constant tube currentscan derived by convolving the dose rate profile with tube currentprofiles. Reference character (d) of FIG. 5 illustrates a graph showingthe dose ratio derived by dividing the accumulated dose profile of theTCM scan by the constant tube current scan.

The organ-specific CTDI_(vol) factor may be computed as

$\begin{matrix}{{\left( {CTDI}_{vol} \right)_{{organ},{convolution}} = {R_{organ}{CTDI}_{vol}}},} & (1) \\{{R_{organ} = \frac{\sum\limits_{z \in {\{{organ}\}}}^{\;}{{Dose}\mspace{14mu} {ratio}_{z}*N_{z}}}{\sum\limits_{z \in {\{{organ}\}}}^{\;}N_{z}}},} & (2)\end{matrix}$

where CTDI_(vol) refers to the CTDI_(vol) reported on the CT scannerconsole, which is derived using the average mAs of the CT exam.R_(organ) represents the dose field difference between the specific TCMexam and the constant mAs condition. Dose ration_(z) is the dose ratiovalue at location z, and N is the number of organ voxels in the axialslice at location z. Such organ-specific CTDI_(vol) can be regarded as aregional CTDI_(vol) that reflects the difference of the strength of thedose field between TCM and constant mAs for a specific organ. It is usedas an adjustment factor to account for the regional dose field.

The method of FIG. 2 includes estimating 208 an irradiation dose forapplication to the patient by the X-ray system based on the radiationfield model and the determined patient anatomy model. Continuing theaforementioned example, the computing device 113 may estimate anirradiation dose for application to the patient by the X-ray systembased on the radiation field model and the determined patient anatomymodel.

With effective methods to approximate the patient anatomy and radiationfield, dose to each organ of a patient undergoing a CT examination withTCM, H_(organ), can be estimated as

H _(TCM) =h _(organ)*(CTDI _(vol))_(organ,convolution).  (3)

In a study, the accuracy of the proposed estimation methods may beevaluated by incorporating tube current modulation into the Monte Carloprogram and estimating the organ dose across the 58 patient models asthe gold standard. As noted earlier, each patient case can be matched toan XCAT model based on trunk height. The organ dose can subsequently beestimated using the proposed patient matching and convolution methodunder five modulation strengths. The gold standard for the comparisonwas the organ dose for the original phantom with TCM explicitly modeledin the MC simulation. The accuracy of abdominopelvic exam in one case isshown in FIG. 6, which illustrates graphs showing a histogram of errorin predicting organ dose for the abdominopelvic scans. In FIG. 6, thex-axis is determined as the difference between the estimated and actualorgan doses normalized by the CTDI_(vol) of the exam.

FIG. 7 illustrates a screen display of an example progress reportshowing a written record in accordance with embodiments of the presentdisclosure.

As discussed, prediction and estimation of quality and safety aspects ofan imaging exam can require a priori knowledge of internal geometricalattributes or characteristics of a patient. For example, a computingdevice may use organ location, size, dimension, and the like. Thisinformation or data can be used to match the patient to one or moreanatomy or virtual models of humans through various suitable techniques.In an example, a patient may be matched to one or more models based onpatient height, weight, gender, body mass index (BMI), racial profile,the like, or combinations thereof. In another example, a patient may bematched to one or more models based on a cross correlation of single- ormultiple-view “scout” (2D) images to like synthetic images from virtualmodels. In other examples, a patient may be matched based on measured(automatically or manually from 2D or 3D data) patient's chest-height,abdomen-height, and abdominopelvic-height or such to like closest valuesfrom virtual models. In other example, a patient may be matched based onuse of deep learning techniques based on prior manually-matched modelsfor devising a stochastic predictive matching model. Further, any ofthese techniques, example, or combinations thereof may be utilized.

In accordance with embodiments, a computing device, such as computingdevice 113 shown in FIG. 1, may suitably determine and present to a userthe accuracy of predictions derived based on matching of the patient tothe models. The accuracy may be based on a measured closeness of thematch or matches. For example, for a given match for an actual patient,a quantification of the match may be determined based on geometricalproperties. Match success can be combined into scalar metric of matchfidelity. The match fidelity may be related to uncertainty in organ doseestimation. Uncertainty in organ dose estimation (confidence intervals)can be ascribed to predicted organ dose values.

FIG. 8 illustrates a graph showing an example relationship of theuncertainty of organ dose estimation to an average distance ofmeasurement mismatch between an actual patient and matched models. Whilethe example shown is linear, this relationship is expected to take anon-linear form that is further a function of the organ and the imagingprotocol used.

It is noted that once a patient is matched to virtual or anatomy models,additional undertakings may involve derivation of organ dose, imagequality values, diagnostic quality, and other such image or patientrelated attributes. This can be based on rapid calculation or look uptables associated with the virtual models undergoing similar proceduresas that for the patient.

The various techniques described herein may be implemented with hardwareor software or, where appropriate, with a combination of both. Thus, themethods and apparatus of the disclosed embodiments, or certain aspectsor portions thereof, may take the form of program code (i.e.,instructions) embodied in tangible media, such as floppy diskettes,CD-ROMs, hard drives, or any other machine-readable storage medium,wherein, when the program code is loaded into and executed by a machine,such as a computer, the machine becomes an apparatus for practicing thepresently disclosed subject matter. In the case of program codeexecution on programmable computers, the computer will generally includea processor, a storage medium readable by the processor (includingvolatile and non-volatile memory and/or storage elements), at least oneinput device and at least one output device. One or more programs may beimplemented in a high level procedural or object oriented programminglanguage to communicate with a computer system. However, the program(s)can be implemented in assembly or machine language, if desired. In anycase, the language may be a compiled or interpreted language, andcombined with hardware implementations.

The described methods and apparatus may also be embodied in the form ofprogram code that is transmitted over some transmission medium, such asover electrical wiring or cabling, through fiber optics, or via anyother form of transmission, wherein, when the program code is receivedand loaded into and executed by a machine, such as an EPROM, a gatearray, a programmable logic device (PLD), a client computer, a videorecorder or the like, the machine becomes an apparatus for practicingthe presently disclosed subject matter. When implemented on ageneral-purpose processor, the program code combines with the processorto provide a unique apparatus that operates to perform the processing ofthe presently disclosed subject matter.

Features from one embodiment or aspect may be combined with featuresfrom any other embodiment or aspect in any appropriate combination. Forexample, any individual or collective features of method aspects orembodiments may be applied to apparatus, system, product, or componentaspects of embodiments and vice versa.

While the embodiments have been described in connection with the variousembodiments of the various figures, it is to be understood that othersimilar embodiments may be used or modifications and additions may bemade to the described embodiment for performing the same functionwithout deviating therefrom. Therefore, the disclosed embodiments shouldnot be limited to any single embodiment, but rather should be construedin breadth and scope in accordance with the appended claims.

While various embodiments of the present disclosure have beenillustrated and described, it will be clear that the present disclosureis not limited to these embodiments only. Numerous modifications,changes, variations, substitutions, and equivalents will be apparent tothose skilled in the art, without departing from the spirit and scope ofthe present disclosure. The singular forms “a,” “an,” and “the” includeplural referents unless the context clearly dictates otherwise. Unlessotherwise indicated, all numbers expressing quantities of distance,frequencies, and so forth used in the specification and claims are to beunderstood as being modified in all instances by the term “about.”Accordingly, unless indicated to the contrary, the numerical parametersset forth in this specification and attached claims are approximationsthat can vary depending upon the desired properties sought to beobtained by the presently disclosed subject matter.

1. A method comprising: providing a database of patient anatomy models;providing a radiation field model of an X-ray system; receiving ameasure of an anatomy of a patient; determining a patient anatomy modelamong the patient anatomy models that matches or is similar to theanatomy of the patient based on the measure of the patient and acorresponding measure of each of the patient anatomy models; andestimating an irradiation dose for application to the patient by theX-ray system based on the radiation field model and the determinedpatient anatomy model.
 2. The method of claim 1, wherein the database ofpatient anatomy models includes patient anatomy models of differingages, sizes, and genders.
 3. The method of claim 1, further comprising:receiving image data of a plurality of patients; identifying internalstructures of the patients based on the image data; and generating thepatient anatomy models based on the identified internal structures. 4.The method of claim 3, wherein the image is a computed tomography (CT)image.
 5. The method of claim 3, wherein identifying internal structurescomprises segmenting bones and organs within an image volume of theimage data.
 6. The method of claim 3 further comprising morphingstructures from other patient anatomy models to identify other internalstructures, and wherein generating the patient anatomy models comprisesusing the identified other internal structures to generate the patientanatomy models.
 7. The method of claim 1, further comprising: applyingautomated segmentation and anatomical benchmarks on a topogram image ofthe patient; registering the anatomical benchmarks and the major organsagainst the patient anatomy models; and auto-correlating between thetopogram image of the patient and the patient anatomy models.
 8. Themethod of claim 1, wherein receiving a measure of an anatomy of apatient comprises receiving a measure of a distance between a clavicleof the patient to a pelvic region of the patient.
 9. The method of claim8, wherein the measure of the distance is a distance between the top ofthe clavicle and an end of the pelvic region.
 10. The method of claim 1,further comprising: receiving a tomogram image of the patient; anddetermining the measure of the anatomy of the patient based on thetomogram image.
 11. The method of claim 10, wherein the database ofpatient anatomy models includes extended cardiac-torso (XCAT) phantomsor similar patient models, and wherein determining a patient anatomymodel comprises determining the patient anatomy model among the patientanatomy models that matches or is similar to the anatomy of the patientbased on the distance between the top of the clavicle and the end of thepelvic region in the tomogram image as compared to the correspondingmeasure in the XCAT phantoms or similar anatomical models.
 12. Themethod of claim 1, wherein the radiation model comprises at least one ofa geometry of the X-ray system, X-ray tube motion of the X-ray system, acharacteristic of a bowtie filter of the X-ray system, peak kilovoltage(kVp) of the X-ray system, and a peak milliampere (mA) of the X-raysystem.
 13. The method of claim 1, wherein the X-ray system comprises acomputed tomography imaging system.
 14. The method of claim 1, furthercomprising determining an X-ray distribution created by dynamic tubecurrent changes of the X-ray system to generate the radiation fieldmodel.
 15. The method of claim 14, wherein determining an X-raydistribution comprises: determining a dose rate profile based on MonteCarlo simulation; convolving the dose rate profile with TCM and constanttube profiles to generate accumulated z-dimensional dose distributionsfor each condition; determining accumulated dose distributions under TCMand constant tube current conditions; overlaying the accumulated dosedistributions with patient organ distributions identified in the patientanatomy models; determining a regional dose index for organs to accountfor a local dose field; and multiplying the dose index withCTDI_(vol)-normalized organ dose coefficients under constant tubecurrent to approximate organ dose for TCM computed tomographyexamination.
 16. The method of claim 1, wherein estimating theirradiation dose comprises estimating the irradiation does of thepatient undergoing computed tomography examination.
 17. A systemcomprising: at least one computing device including at least oneprocessor and memory configured to: provide a database of patientanatomy models; provide a radiation field model of an X-ray system;receive a measure of an anatomy of a patient; determine a patientanatomy model among the patient anatomy models that matches or issimilar to the anatomy of the patient based on the measure of thepatient and a corresponding measure of each of the patient anatomymodels; and estimate an irradiation dose for application to the patientby the X-ray system based on the radiation field model and thedetermined patient anatomy model.
 18. The system of claim 17, whereinthe database of patient anatomy models includes patient anatomy modelsof differing ages, sizes, and genders.
 19. The system of claim 17,wherein the at least one computing device is configured to: receiveimage data of a plurality of patients; identify internal structures ofthe patients based on the image data; and generate the patient anatomymodels based on the identified internal structures.
 20. The system ofclaim 19, wherein the image is a computed tomography (CT) image. 21-34.(canceled)